Early Detection of Fusarium oxysporum Infection in Processing Tomatoes (Solanum lycopersicum) and Pathogen-Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling

Recent advances in signal processing algorithms for electronic noses

Electronic nose (e-nose) technology has emerged as a pivotal tool in various domains, which has been widely utilized for odor identification, concentration evaluation, and prediction tasks. This review provides a comprehensive survey on the most recent advances in the development of e-nose systems and their algorithmic applications, emphasizing the roles of various methodologies and deep learning technologies in odor classification and concentration forecasting. Additionally, we delve into model evaluation methods, including multidimensional performance assessment and cross-validation. Future trends encompass broader application domains, advanced drift correction techniques, comprehensive multifactorial analysis, and enhanced capabilities for dealing with unknown interferents. These trends are set to propel significant breakthroughs in e-nose technology within scientific research and practical applications, solidifying the e-nose system as a crucial tool in many areas such as environmental monitoring, biomedicine, and public safety.

Distinguishing between Wheat Grains Infested by Four Fusarium Species by Measuring with a Low-Cost Electronic Nose

An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium: F. avenaceum, F. langsethiae, F. poae, and F. sporotrichioides. The electronic nose prototype is a low-cost device based on commercially available TGS series sensors from Figaro Corp. Two types of gas sensors that respond to the perturbation are used to collect signals useful for discriminating between the samples under study. First, an electronic nose detects the transient response of the sensors to a change in operating conditions from clean air to the presence of the gas being measured. A simple gas chamber was used to create a sudden change in gas composition near the sensors. An inexpensive pneumatic system consisting of a pump and a carbon filter was used to supply the system with clean air. It was also used to clean the sensors between measurement cycles. The second function of the electronic nose is to detect the response of the sensor to temperature disturbances of the sensor heater in the presence of the gas to be measured. It has been shown that features extracted from the transient response of the sensor to perturbations by modulating the temperature of the sensor heater resulted in better classification performance than when the machine learning model was built from features extracted from the response of the sensor in the gas adsorption phase. By combining features from both phases of the sensor response, a further improvement in classification performance was achieved. The E-nose enabled the differentiation of F. poae from the other fungal species tested with excellent performance. The overall classification rate using the Support Vector Machine model reached 70 per cent between the four fungal categories tested.

Antioxidant and Antifungal Properties of Cinnamon, Cloves, Melia azedarach L. and Ocimum gratissimum L. Extracts against Fusarium oxysporum Isolated from Infected Vegetables in Mauritius

BACKGROUND

Fusarium species, a group of economically destructive phytopathogens, are poorly studied in Mauritius where agriculture holds much significance. Furthermore, the increasing popularity of organic farming has prompted interest in alternatives to chemical fungicides.

METHODS

After gaining an overview of Fusarium prevalence in Mauritius fields through a survey, the pathogen was isolated from infected crops and identified based on morphological and molecular characteristics. Methanol and water extracts were then prepared from Melia azedarach, Ocimum gratissimum, cinnamon and cloves before determining their phytochemical profiles. Additionally, the antioxidant and antifungal effects of different concentrations of aqueous extracts were assessed.

RESULTS

The isolate was confirmed as Fusarium oxysporum, and cloves inhibited its growth by up to 100%, especially at 60 and 90 g/L, with the results being significantly higher than those of the synthetic fungicide mancozeb. Over 50% inhibition was also noted for cinnamon and Ocimum gratissimum, and these effects could be linked to the flavonoids, phenols and terpenoids in the extracts.

CONCLUSION

This study presented the aqueous extracts of cloves, cinnamon and Ocimum gratissimum as potential alternatives to chemical fungicides. It also confirmed the prevalence of Fusarium infection in Mauritius fields, thereby highlighting the need for additional studies on the pathogen.

Integrated Management of Pathogens and Microbes in Cannabis sativa L. (Cannabis) under Greenhouse Conditions

The increased cultivation of high THC-containing Cannabis sativa L. (cannabis), particularly in greenhouses, has resulted in a greater incidence of diseases and molds that can negatively affect the growth and quality of the crop. Among them, the most important diseases are root rots (Fusarium and Pythium spp.), bud rot (Botrytis cinerea), powdery mildew (Golovinomyces ambrosiae), cannabis stunt disease (caused by hop latent viroid), and a range of microbes that reduce post-harvest quality. An integrated management approach to reduce the impact of these diseases/microbes requires combining different approaches that target the reproduction, spread, and survival of the associated pathogens, many of which can occur on the same plant simultaneously. These approaches will be discussed in the context of developing an integrated plan to manage the important pathogens of greenhouse-grown cannabis at different stages of plant development. These stages include the maintenance of stock plants, propagation through cuttings, vegetative growth of plants, and flowering. The cultivation of cannabis genotypes with tolerance or resistance to various pathogens is a very important approach, as well as the maintenance of pathogen-free stock plants. When combined with cultural approaches (sanitation, management of irrigation, and monitoring for diseases) and environmental approaches (greenhouse climate modification), a significant reduction in pathogen development and spread can be achieved. The use of preventive applications of microbial biological control agents and reduced-risk biorational products can also reduce disease development at all stages of production in jurisdictions where they are registered for use. The combined use of promising strategies for integrated disease management in cannabis plants during greenhouse production will be reviewed. Future areas for research are identified.

Analysis of Wheat Grain Infection by Fusarium Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR

Fusarium graminearum and F. culmorum are considered some of the most dangerous pathogens of plant diseases. They are also considerably dangerous to humans as they contaminate stored grain, causing a reduction in yield and deterioration in grain quality by producing mycotoxins. Detecting Fusarium fungi is possible using various diagnostic methods. In the manuscript, qPCR tests were used to determine the level of wheat grain spoilage by estimating the amount of DNA present. High-performance liquid chromatography was performed to determine the concentration of DON and ZEA mycotoxins produced by the fungi. GC-MS analysis was used to identify volatile organic components produced by two studied species of Fusarium. A custom-made, low-cost, electronic nose was used for measurements of three categories of samples, and Random Forests machine learning models were trained for classification between healthy and infected samples. A detection performance with recall in the range of 88-94%, precision in the range of 90-96%, and accuracy in the range of 85-93% was achieved for various models. Two methods of data collection during electronic nose measurements were tested and compared: sensor response to immersion in the odor and response to sensor temperature modulation. An improvement in the detection performance was achieved when the temperature modulation profile with short rectangular steps of heater voltage change was applied.

Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species

Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be important for selecting effective preventive measures against their spread. A low-cost electronic nose applying six commercially available TGS-series gas sensors from Figaro Inc. was used in our research. Different modes of operation of the electronic nose were applied and compared, namely, gas adsorption and desorption, as well as modulation of the sensor's heating voltage. Classification models using the random forest technique were applied to differentiate between measured sample categories of four species: F. avenaceum, F. culmorum, F. greaminarum, and F. oxysporum. In our research, it was found that the mode of operation with modulation of the heating voltage had the advantage of collecting data from which features can be extracted, leading to the training of machine learning classification models with better performance compared to cases where the sensor's response to the change in composition of the measured gas was exploited. The optimization of the data collection time was investigated and led to the conclusion that the response of the sensor at the beginning of the heating voltage modulation provides the most useful information. For sensor operation in the mode of gas desorption/absorption (i.e., modulation of the gas composition), the optimal time of data collection was found to be longer.

Design of a Multisensory Device for Tomato Volatile Compound Detection Based on a Mixed Metal Oxide-Electrochemical Sensor Array and Optical Reader

Insufficient control of tomato ripening before harvesting and infection by fungal pests produce large economic losses in world tomato production. Aroma is an indicative parameter of the state of maturity and quality of the tomato. This study aimed to design an electronic system (TOMATO-NOSE) consisting of an array of 12 electrochemical sensors, commercial metal oxide semiconductor sensors, an optical camera for a lateral flow reader, and a smartphone application for device control and data storage. The system was used with tomatoes in different states of ripeness and health, as well as tomatoes infected with Botrytis cinerea. The results obtained through principal component analysis of the olfactory pattern of tomatoes and the reader images show that TOMATO-NOSE is a good tool for the farmer to control tomato ripeness before harvesting and for the early detection of Botrytis cinerea.